Climate control of terrestrial carbon exchange across biomes and continents

Home Search Collections Journals About Contact us My IOPscience Climate control of terrestrial carbon exchange across biomes and continents This article has been downloaded from IOPscience. Please scroll down to see the full text article. 2010 Environ. Res. Lett. 5 034007 (http://iopscience.iop.org/1748-9326/5/3/034007) View the table of contents for this issue, or go to the journal homepage for more Download details: IP Address: 166.2.95.94 The article was downloaded on 08/05/2013 at 21:51 Please note that terms and conditions apply. IOP PUBLISHING ENVIRONMENTAL RESEARCH LETTERS Environ. Res. Lett. 5 (2010) 034007 (10pp) doi:10.1088/1748-9326/5/3/034007 Climate control of terrestrial carbon exchange across biomes and continents Chuixiang Yi1 , Daniel Ricciuto2 , Runze Li3 , John Wolbeck1, Xiyan Xu1 , Mats Nilsson4 , Luis Aires5,117 , John D Albertson6,117, Christof Ammann7,117, M Altaf Arain8,117, Alessandro C de Araujo9,117, Marc Aubinet10,117, Mika Aurela11,117, Zoltán Barcza12,117 , Alan Barr13,117 , Paul Berbigier14,117, Jason Beringer15,117, Christian Bernhofer16,117, Andrew T Black17,117 , Paul V Bolstad18,117 , Fred C Bosveld19,117, Mark S J Broadmeadow20,117, Nina Buchmann21,117, Sean P Burns22,117, Pierre Cellier23,117, Jingming Chen24,117, Jiquan Chen25,117, Philippe Ciais26,117 , Robert Clement27,117, Bruce D Cook28,117, Peter S Curtis29,117, D Bryan Dail30,117 , Ebba Dellwik31,117, Nicolas Delpierre32,117, Ankur R Desai33,117, Sabina Dore34,117, Danilo Dragoni35,117, Bert G Drake36,117, Eric Dufrêne32,117, Allison Dunn37,117, Jan Elbers38,117, Werner Eugster21,117, Matthias Falk39,117, Christian Feigenwinter40,117, Lawrence B Flanagan41,117 , Thomas Foken42,117, John Frank43,117, Juerg Fuhrer7,117, Damiano Gianelle44,117 , Allen Goldstein45,117 , Mike Goulden46,117, Andre Granier47,117, Thomas Grünwald48,117, Lianhong Gu2,117 , Haiqiang Guo49,117, Albin Hammerle50,117, Shijie Han51,117 , Niall P Hanan52,117, László Haszpra53,117, Bernard Heinesch10,117, Carole Helfter54,117, Dimmie Hendriks55,117, Lindsay B Hutley56,117 , Andreas Ibrom57,117, Cor Jacobs38,117, Torbjörn Johansson58,117, Marjan Jongen59,117, Gabriel Katul60,117, Gerard Kiely61,117 , Katja Klumpp62,117, Alexander Knohl21,117 , Thomas Kolb34,117, Werner L Kutsch63,117, Peter Lafleur64,117, Tuomas Laurila11,117 , Ray Leuning65,117, Anders Lindroth58,117, Heping Liu66,117 , Benjamin Loubet23,117, Giovanni Manca67,117, Michal Marek68,117, Hank A Margolis69,117 , Timothy A Martin70,117, William J Massman43,117, Roser Matamala71,117, Giorgio Matteucci72,117, Harry McCaughey73,117, Lutz Merbold74,117, Tilden Meyers75,117, Mirco Migliavacca76,117, Franco Miglietta77,117, Laurent Misson,78,117,118, Meelis Mölder58,117, John Moncrieff27,117, Russell K Monson79,117, Leonardo Montagnani80,81,117, Mario Montes-Helu34,117, Eddy Moors82,117, Christine Moureaux10,83,117, Mukufute M Mukelabai84,117, J William Munger85,117, May Myklebust65,117, Zoltán Nagy86,117 , Asko Noormets87,117, Walter Oechel88,117, Ram Oren89,117, Stephen G Pallardy90,117 , Kyaw Tha Paw U39,117 , João S Pereira59,117, Kim Pilegaard57,117, Krisztina Pintér86,117, Casimiro Pio91,117 , Gabriel Pita92,117 , Thomas L Powell93,117 , Serge Rambal94,117, James T Randerson46,117, Celso von Randow95,117, Corinna Rebmann64,117, Janne Rinne96,117, Federica Rossi77,117 , Nigel Roulet97,117, Ronald J Ryel98,117, Jorgen Sagerfors4,117, Nobuko Saigusa99,117, Marı́a José Sanz100,117, Giuseppe-Scarascia Mugnozza101,117, Hans Peter Schmid102,117, Guenther Seufert103,117, Mario Siqueira89,117, Jean-François Soussana62,117, Gregory Starr104,117, Mark A Sutton105,117, John Tenhunen106,117, Zoltán Tuba,86,117,118, Juha-Pekka Tuovinen11,117, Riccardo Valentini107,117, Christoph S Vogel108,117 , Jingxin Wang109,117, Shaoqiang Wang110,117, Weiguo Wang111,117, Lisa R Welp112,117, Xuefa Wen110,117, Sonia Wharton113,117, Matthew Wilkinson20,117, Christopher A Williams114,117 , 1748-9326/10/034007+10$30.00 1 © 2010 IOP Publishing Ltd Printed in the UK Environ. Res. Lett. 5 (2010) 034007 C Yi et al Georg Wohlfahrt50,117, Susumu Yamamoto115,117, Guirui Yu110,117, Roberto Zampedri44,117, Bin Zhao49,117 and Xinquan Zhao116,117 1 School of Earth and Environmental Sciences, Queens College, City University of New York, NY 11367, USA 2 Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA 3 Department of Statistics, Pennsylvania State University, University Park, PA 16802, USA 4 Department of Forest Ecology, The Swedish University of Agricultural Sciences, SE-901 83 Umeå, Sweden 5 CESAM and Department of Environmental Engineering, School of Technology and Management, Polytechnic Institute of Leiria, Portugal 6 Department of Civil and Environmental Engineering, Duke University, Durham, NC 22708-0287, USA 7 Federal Research Station Agroscope Reckenholz-Tänikon, Reckenholzstrasse 191, 8046 Zürich, Switzerland 8 School of Geography and Earth Sciences, McMaster University, Hamilton, ON, L8S 4K1, Canada 9 Instituto Nacional de Pesquisas da Amazonia, Programa LBA, Campus-II, Manaus—Amazonas 69060, Brazil 10 University of Liege, Gembloux Agro-Bio Tech, Unit of Biosystem Physics, 2 Passage des Déportés, 5030 Gembloux, Belgium 11 Finnish Meteorological Institute, Climate Change Research, FI-00101 Helsinki, Finland 12 Department of Meteorology, Eötvös Loránd University, H-1117 Budapest, Pázmány sétány 1/A, Hungary 13 Climate Research Division, Environment Canada, Saskatoon, SK, S7N 3H5, Canada 14 INRA, UR1263 EPHYSE, Villenave d’Ornon F-33883, France 15 School of Geography and Environmental Science, Monash University, Clayton, Victoria 3800, Australia 16 Institute of Hydrology and Meteorology, Dresden University of Technology, Pienner Straße 23, D-01737, Tharandt, Germany 17 Land and Food Systems, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada 18 University of Minnesota, 115 Green Hall 1530 Cleveland Avenue N. St Paul, MN 55108, USA 19 Royal Netherlands Meteorological Institute, 3730 AE De Bilt, The Netherlands 20 Forest Research, Alice Holt Lodge, Farnham, Surrey GU10 4LH, UK 21 ETH, Zurich, Institute of Plant Science, Universitaetsstrasse 2, Zuerich 8092, Switzerland 22 National Center for Atmospheric Research, Boulder, CO 80307-3000, USA 23 UMR INRA-INA PG—Environment and Arable Crops Unit 78850 Thiverval-Grignon, France 24 Department of Geography, University of Toronto, Toronto, ON, M5S 3G3, Canada 25 Department of Environmental Sciences, University of Toledo, Toledo, OH 43606-3390, USA 26 LSCE, UMR CEA-CNRS, Batiment 709, CE, L‘Orme des Merisiers, F-91191 Gif-sur-Yvette, France 27 School of GeoSciences, The University of Edinburgh, Mayfield Road, Edinburgh, EH9 3JU, UK 28 Biospheric Sciences Branch, NASA’s Goddard Space Flight Center, Greenbelt, MD 20771, USA 29 Department of Evolution, Ecology, and Organismal Biology, Ohio State University, Columbus, OH 43210, USA 30 Department of Plant, Soil, and Environmental Science, University of Maine, Orono, ME 04469, USA 31 Wind Energy Division, Risø National Laboratory for Sustainable Energy, Technical University of Denmark, PO 49, DK-4000 Roskilde, Denmark 32 Université Paris-Sud, Bâtiment 362, Ecologie, Systematique et Evolution, Orsay Cedex F-91405, France 33 Department of Atmospheric and Oceanic Sciences, University of Wisconsin, Madison, WI 53706, USA 34 Northern Arizona University, School of Forestry Northern Arizona University, Flagstaff, AZ 86001, USA 35 Atmospheric Science Program, Department of Geography, Indiana University, Bloomington, IN 47405, USA 36 Smithsonian Environmental Research Center, Edgewater, MD 21037, USA 37 Department of Physical and Earth Science, Worcester State College, 486 Chandler Street Worcester, MA 01602, USA 38 ESS-CC, Alterra Wageningen UR, 6700 AA Wageningen, The Netherlands 39 Atmospheric Science Group, LAWR, UC Davis, Davis, CA 95616, USA 2 Environ. 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Lett. 5 (2010) 034007 C Yi et al 40 Institute for Meteorology, Climatology and Remote Sensing, University of Basel, Klingelbergstrasse 27, CH-4056 Basel, Switzerland 41 Department of Biological Sciences, University of Lethbridge, 4401 University Drive, Lethbridge, AB, T1K 3M4, Canada 42 Department of Micrometeorology, University of Bayreuth, 95440 Bayreuth, Germany 43 USDA Forest Service, Rocky Mountain Research Station, 240 West Prospect, Fort Collins, CO 80526, USA 44 IASMA Research and Innovation Centre, Fondazione E Mach, Environment and Natural Resources Area, San Michele all’Adige, I38010 Trento, Italy 45 Department of Environmental Science, Policy and Management, University of California, Berkeley, CA 94720, USA 46 Department of Earth System Science, University of California, Irvine, CA 92697, USA 47 INRA, UMR 1137 Ecologie et Écophysiologie Forestierès, F54280, Champenoux, France 48 Technische Universität Dresden, Institute of Hydrology and Meteorology, Department of Meteorology, Piennerstrasse 9, 01737 Tharandtt, Germany 49 Ministry of Education Key Laboratory for Biodiversity Science and Ecological Engineering, Institute of Biodiversity Science, Fudan University, Shanghai 200433, People’s Republic of China 50 University of Innsbruck, Institute of Ecology Sternwartestrasse 15, Innsbruck 6020, Austria 51 Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, People’s Republic of China 52 Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO 80523, USA 53 Hungarian Meteorological Service, H-1675 Budapest, PO Box 39, Hungary 54 Centre for Ecology and Hydrology (Edinburgh) Bush Estate Penicuik, Midlothian, EH26 0QB, UK 55 Department of Hydrology and Geo-Environmental Sciences, Boelelaan 1085, 1081 HV, VU University Amsterdam, The Netherlands 56 School of Environmental and Life Sciences, Charles Darwin University, Darwin, NT 0909, Australia 57 Biosystems Division, Risø National Laboratory for Sustainable Energy, Technical University of Denmark, PO 49, DK-4000 Roskilde, Denmark 58 Geobiosphere Science Centre, Physical Geography and Ecosystems Analysis, Lund University, Sölvegatan 12, SE-223 62 Lund, Sweden 59 Instituto Superior de Agronomia, Universidade Técnica de Lisboa, Tapada da Ajuda 1349-017 Lisboa, Portugal 60 School of the Environment, Duke University, Durham, NC 27708-0328, USA 61 Civil and Environmental Engineering Department, University College Cork, Cork, Republic of Ireland 62 INRA, Unité d‘Agronomie, 234, Avenue du Brézet, F-63000 Clermont-Ferrand, France 63 Johann Heinrich von Thünen-Institut (vTI), Institut für Agrarrelevante Klimaforschung, Bundesallee 50, 38116 Braunschweig, Germany 64 Department of Geography, Trent University, Peterborough, ON, K9J 7B8, Canada 65 CSIRO Marine and Atmospheric Research, PO Box 3023, Canberra, ACT, 2601, Australia 66 Department of Physics, Atmospheric Sciences and Geoscience, Jackson State University, Jackson, MS 39217, USA 67 Rende Division, Institute for Atmospheric Pollution, Consiglio Nazionale delle Ricerche, 87036 Rende, Italy 68 Institute of Systems Biology and Ecology, Division of Ecosystems Processes Lab. of Plants Ecological Physiology, Na Sadkach 7 370 050, Ceske Budejovice, Czech Republic 69 Centre d’études de la forêt Faculté de Foresterie et de Géomatique, Université Laval, QC G1V 0A6, Canada 70 University of Florida, Gainesville, FL 32611, USA 71 Argonne National Laboratory, Biosciences Division, Argonne, IL 60439, USA 72 National Research Council, Institute of Agroenvironmental and Forest Biology, 00015 Monterotondo Scalo (RM), Italy 73 Department of Geography, Queen’s University, Kingston, ON, K7L 3N6, Canada 74 Max-Planck Institute for Biogeochemie, Jena, D-07745, Germany 75 NOAA/ATDD, Oak Ridge, TN 37831-2456, USA 76 Remote Sensing of Environmental Dynamics Laboratory, DISAT, Università degli Studi di Milano-Bicocca, Italy 77 CNR-IBIMET, Istituto di Biometeorologia, via Giovanni Caproni 8, 50145 Firenze, Italy 78 CNRS-CEFE, 1919 route de Mende, 34293 Montpellier Cedex 5, France 79 Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, CO 80309, USA 80 Servizi Forestali, Agenzia per l’Ambiente, Provincia Autonoma di Bolzano, 39100 Bolzano, Italy 3 Environ. 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Lett. 5 (2010) 034007 C Yi et al 81 Faculty of Sciences and Technologies, Free University of Bozen-Bolzano, Piazza Università 1, 39100, Bolzano, Italy 82 Alterra Green World Research, Wageningen, NL 6700 AA, The Netherlands 83 University of Liege, Gembloux Agro-Bio Tech, Unit of Crops Management, 2 Passage des Déportés, 5030 Gembloux, Belgium 84 Zambian Meteorological Department, Western Province, Mongu, Zambia 85 Division of Engineering and Applied Science, Department of Earth and Planetary Science, Harvard University, Cambridge, MA 02138, USA 86 Institute of Botany and Ecophysiology, Agricultural University of Gödöllô, H-2103 Gödöllô, Páter Károly u. 1, Hungary 87 Department of Forestry and Environmental Resources, North Carolina State University, NC 29695, USA 88 Department of Biology, San Diego State University, San Diego, CA 92182-4614, USA 89 Nicholas School of the Environment and Earth Sciences, Duke University, Durham, NC 27708-0328, USA 90 Department of Forestry, University of Missouri, Columbia, MO 65211, USA 91 CESAM and Department of Environment, University of Aveiro, Aveiro 3810-193, Portugal 92 Instituto Superior Tecnico, Mechanical Engineering Department, 1049-001 Lisboa, Portugal 93 The Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA 94 DREAM, CEFE, CNRS, UMR5175, 1919 route de Mende, F-34293 Montpellier Cedex 5, France 95 Earth System Science Center, National Institute of Space Research, Cachoeira Paulista, SP 12630, Brazil 96 Department of Physics, FI-00014, University of Helsinki, Finland 97 Department of Geography, McGill University 805, Sherbrooke Street West Montréal, QC, H3A 2K6, Canada 98 Department of Wildland Resources, Utah State University, Logan, UT 84322-5230, USA 99 Center for Global Environmental Research, National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba 305-8506, Japan 100 Centro de Estudios Ambientales del Mediterraneo, Parque Tecnologico, Charles H Darwin 14, E-46980 Paterna, Spain 101 Agricultural Research Council, Department of Agronomy, Forestry and Land Use, 00184 Rome, Italy 102 Atmospheric Environmental Research Institute of Meteorology and Climate Research, Forschungszentrum Karlsruhe, Garmisch-Partenkirchen, Germany 103 Institute for Environment and Sustainability, Joint Research Center European Commission, TP 280, I-21020 Ispra, Italy 104 Department of Biological Sciences, University of Alabama, Tuscaloosa, AL 35487-0206, USA 105 Atmospheric Sciences Centre for Ecology and Hydrology (CEH), Bush Estate, Penicuik, Midlothian, EH26 0QB, UK 106 Department of Plant Ecology, University of Bayreuth, 95440 Bayreuth, Germany 107 Department of Forest Environment and Resources, University of Tuscia, I-01100 Viterbo, Italy 108 The University of Michigan Biological Station, Pellston, MI 49769, USA 109 School of Mathematics, Liaoning Normal University, Dalian 116039, People’s Republic of China 110 Institute of Geographic Sciences and Natural Resource Research, Chinese Academy of Science, Beijing 100101, People’s Republic of China 111 IMSG@National Center for Environmental Predictions, NOAA, Camp Springs, MD 20746, USA 112 Geosciences Research Division, Scripps Institution of Oceanography, University of California, La Jolla, CA 92093, USA 113 Atmospheric, Earth and Energy Division, Lawrence Livermore National Laboratory, Livermore, CA 94551, USA 114 Graduate Degree Program in Geography, Clark University, Worcester, MA 01610-1477, USA 115 Okayama University, Okayama 700-8530, Japan 116 Northwest Plateau Institute of Biology, Chinese Academy of Sciences, Xining 810001 Qinghai, People’s Republic of China Received 10 June 2010 Accepted for publication 27 July 2010 Published 16 August 2010 Online at stacks.iop.org/ERL/5/034007 4 Environ. Res. Lett. 5 (2010) 034007 C Yi et al Abstract Understanding the relationships between climate and carbon exchange by terrestrial ecosystems is critical to predict future levels of atmospheric carbon dioxide because of the potential accelerating effects of positive climate–carbon cycle feedbacks. However, directly observed relationships between climate and terrestrial CO2 exchange with the atmosphere across biomes and continents are lacking. Here we present data describing the relationships between net ecosystem exchange of carbon (NEE) and climate factors as measured using the eddy covariance method at 125 unique sites in various ecosystems over six continents with a total of 559 site-years. We find that NEE observed at eddy covariance sites is (1) a strong function of mean annual temperature at mid- and high-latitudes, (2) a strong function of dryness at mid- and low-latitudes, and (3) a function of both temperature and dryness around the mid-latitudinal belt (45◦ N). The sensitivity of NEE to mean annual temperature breaks down at ∼16 ◦ C (a threshold value of mean annual temperature), above which no further increase of CO2 uptake with temperature was observed and dryness influence overrules temperature influence. Keywords: NEE, climate control, terrestrial carbon sequestration, temperature, dryness, eddy flux, biomes, photosynthesis, respiration, global carbon cycle S Online supplementary data available from stacks.iop.org/ERL/5/034007/mmedia and seasonal timescales (Law et al 2002). Although several synthesis efforts have been conducted across eddy-flux tower sites, the role of climatic drivers in causing NEE variability across multiple sites on annual or longer timescales is still not clear (Law et al 2002, Valentini et al 2000, Reichstein et al 2007). Determining the environmental controls on NEE is complicated because NEE is the difference between photosynthesis and ecosystem respiration, and climate variations may affect these two components in different ways. Spatial variability in respiration is strongly correlated with temperature, precipitation and substrate supply (Raich et al 2002, Ryan and Law 2005), and gross primary productivity has been shown to be subject to climate-based limiting factors—temperature, precipitation and/or radiation, depending on the region (Law et al 2002). This paper seeks to identify the climate controls on spatial NEE variability globally as represented within FLUXNET, a global network of eddy covariance tower sites (Baldocchi et al 2001). Other studies have shown that nonclimate factors, especially disturbance, are a major factor causing NEE variability (Oren et al 2006, Thornton et al 2002, Foley et al 2005). The role of disturbance history may be underplayed in FLUXNET synthesis studies because the number of recently disturbed sites is limited. However, we expect that other recent estimates that emphasize the effects of other non-climate factors such as nitrogen (Magnani et al 2007, Sutton et al 2008) have downplayed the role of climatic interactions. 1. Introduction Determining the relationships between terrestrial carbon exchange and climate is fundamentally important because climate–carbon cycle feedback could significantly accelerate (or decelerate) future climate warming (Zeng et al 2004, 2005). Globally, the observed growth rate anomaly of atmospheric CO2 concentration is correlated with the multivariate El NiñoSouthern Oscillation index (Heimann and Reichstein 2008). Inversion modeling (Bousquet et al 2000) and biome-based analyses of climate anomalies (Zhou et al 2008) suggest that the oceanic carbon reservoir is a minor player in this variability. Instead, variations in the atmospheric CO2 growth rate result largely from the impact of climate on terrestrial carbon sequestration (Nemani et al 2003, Xiao and Moody 2004), including regional impacts of extreme climate conditions such as heat waves and droughts (Ciais et al 2005, Xiao et al 2009). On much smaller spatial scales, large amounts of data have been collected continuously over the last two decades using the eddy covariance technique to measure directly the net ecosystem exchange of CO2 (NEE) between the biosphere and the atmosphere (Baldocchi et al 2001, Law et al 2002). Although a typical eddy covariance footprint is relatively small (ca. 1 km2 ), NEE variability at these sites is often representative of variability over much larger spatial scales as a result of the spatial coherence of climate anomalies (Ciais et al 2005, Nemani et al 2003, Xiao and Moody 2004). These temporal variations in NEE, the imbalance between photosynthesis (fixation of atmospheric carbon dioxide into organic carbon) and ecosystem respiration (plant and microbial respiration converting organic carbon into atmospheric carbon dioxide), are caused predominately by climatic drivers on daily 2. Data and sites The present analysis is based on 559 site-years of eddy covariance data measured from 125 sites throughout the world from 1992 to 2008 (supplementary table S1 available at stacks.iop.org/ERL/5/034007/mmedia). The latitudes 117 These authors are listed alphabetically and contributed equally to this work. 118 Deceased. 5 Environ. Res. Lett. 5 (2010) 034007 C Yi et al vary from 37◦ S to 71◦ N, longitudes are broadly covered, and elevation ranges from −2 to 3288 m (supplementary figure S1 available at stacks.iop.org/ERL/5/034007/mmedia). The climatic zones of the sites include polar tundra, maritime temperate, continental temperate, humid subtropical, Mediterranean, arid, semi-arid, tropical monsoon, and tropical wet-and-dry climates. The vegetation types include grassland, evergreen needle-leaf forest, deciduous broad-leaf forest, mixed forest, permanent wetland, open shrubland, closed shrubland, savanna, evergreen broad-leaf forest, and tundra. Stand age ranges from young seedlings to 500 years old (Paw U et al 2004). Sites from all ecosystem types with at least one year of complete NEE and meteorological data are included. NEE and meteorological data used in this analysis are taken from standardized files archived in the FLUXNET-LaThuile database which includes data from the AmeriFlux, Fluxnet-Canada, CARBOEUROPE, USCCC, ChinaFlux, OzFlux, CarboAfrica, and AsiaFlux networks. These data have been quality controlled and gap-filled by consistent methods (Papale et al 2006, Moffat et al 2007, Reichstein et al 2005). Meteorological variables used include air temperature, net radiation and precipitation. We have developed a new method to gap-fill the half-hourly meteorological data to produce reliable annual averages (see Methods in the supplementary data available at stacks.iop.org/ERL/5/034007/mmedia). In many cases, the site principal investigators have submitted revised annual NEE estimates based on more detailed, site-specific reanalyses. The data were used in this analysis only in those years when temperature, precipitation, net radiation, and NEE all met the gap-filling criteria (see Methods in the supplementary data available at stacks.iop.org/ERL/5/034007/mmedia). Eddy-flux measurements are inherently uncertain due to: (1) advection errors caused by complex terrain (Aubinet et al 2005, Feigenwinter et al 2008) and complicated canopy structure (Yi 2008); (2) imbalance errors in the energy budget (Massman and Lee 2002, Foken 2008), and (3) the stochastic nature of turbulence (Hollinger and Richardson 2005, Moncrieff et al 1996). These errors have been studied intensively and remain to be quantified exactly for all sites (Reichstein et al 2007). The largest sources of uncertainty that have been quantified in a standardized way in annual NEE result from u ∗ filtering, gap-filling of missing data, and turbulent sampling errors (supplementary materials available at stacks.iop.org/ERL/5/034007/mmedia). objective statistical method to group sites by their dominant climate control. We used a mixture regression model (see supplementary materials available at stacks.iop.org/ERL/5/034007/mmedia) to segregate sites into three groups (supplementary table S1 available at stacks.iop.org/ERL/5/034007/mmedia): (1) Tgroup: variations in NEE are best explained by mean annual temperature alone; (2) D-group: variations in NEE are best explained by a dryness index alone; and (3) B-group: NEE is co-limited by both mean annual temperature and dryness. An independent approach—a nonparametric kernel regression (Wand and Jones 1995) analysis of NEE against mean annual temperature and dryness for all three groups—provides a strong foundation for grouping the sites in this way. The pattern of contour lines in the contour plot for all 125 sites indicates a complex and mixed relationship for temperature and dryness (figure 1(a)), in which NEE at colder sites is generally a function of temperature and at warmer sites is generally a function of dryness. The kernel regression also confirms that the sites are successfully segregated according to their functional dependence. The contour plot for the T-group (figure 1(b)) shows that the contour lines are almost parallel to the dryness index axis. This implies that NEE is a monotonic function of temperature, and that the dryness index does not significantly influence the NEE of the sites in the T-group. The contour plot for the D-group (figure 1(c)) shows that the contour lines are almost parallel to the temperature axis. This implies that NEE is a monotonic function of the dryness index, and that the temperature does not significantly influence the NEE of the sites in the D-group. The contour plot for the Bgroup shows that the contour lines are neither parallel to the temperature axis nor parallel to the dryness index axis. This implies that both the temperature and the dryness index are contributors to the amount of NEE in the sites in the B-group. Moreover, NEE seems to linearly decrease as temperature increases or the dryness index decreases (figure 1(d)). In the T-group, 84% of spatial variations in NEE can be explained by mean annual temperature (figure 2(a)), while in the D-group, 81% of spatial variation in NEE can be accounted for by a dryness index (figure 2(b)). However, in the smaller B-group, NEE is co-limited by mean annual temperature and dryness, and the correlations between the NEE and individual climate factors are relatively weak (figures 3(a) and (b)). We speculate that the variance in NEE unexplained by the climate factors in these three groups is primarily driven by non-climate factors such as stand age, disturbance history, species composition, or canopy leaf area index, reflecting local variation in nutrient and water availability (Raich et al 2002). These non-climate factors are also likely to play a role in the grouping algorithm and account for sites with similar temperature and dryness being grouped differently. 3. Grouping analysis We hypothesize that two direct climatic controls on NEE, temperature and dryness (Budyko 1974), interact in complex ways with non-climatic or indirect climatic factors such as disturbance history, species, soil type and nutrient availability. Although it is not possible to develop a predictive global relationship of NEE with these variables, we ask does the dominant climate factor at individual sites follow distinct geographic patterns? While it is overly simplistic to argue that NEE is a function of two climate variables, it is possible to gain insight into global scale processes through the use of an 4. Discussion and concluding remarks The empirical subdivision of groups also corresponds to latitudinal zonation (supplementary figure S1 available at stacks.iop.org/ERL/5/034007/mmedia): most sites of the temperature-limited group were located in the zones of 6 Environ. Res. Lett. 5 (2010) 034007 C Yi et al Figure 1. Contour plots of site-average NEE (tC ha−1 yr−1 ) of: (a) all the 125 sites; (b) the T-group (47 sites); (c) the D-group (47 sites); and (d) the B-group (32 sites). These contour plots of the regression surface were produced by two-dimensional kernel regression (Wand and Jones 1995) based on the grouping data of the T-group, the D-group, the B-group, and the entire 125 sites (see Methods section and supplementary table S1 available at stacks.iop.org/ERL/5/034007/mmedia). The kernel regression is a commonly used nonparametric regression technique, which assumes the regression function is a smooth function of predictor variables rather than imposing a pre-specific functional form (parametric model) on the regression function. Figure 2. Climatic controls of the site-average net ecosystem exchange (NEE) across the FLUXNET sites (see supplementary table S1 available at stacks.iop.org/ERL/5/034007/mmedia): (a) temperature-limited group; and (b) dryness-limited group. The negative NEE values indicate that atmospheric carbon is assimilated by terrestrial ecosystems, while the positive NEE values indicate that terrestrial organic carbon is converted into atmospheric carbon. The filled circles with mango color in (a) are the site-average NEE of the sites in the prototype T-group with very high posterior probability (>99%) belonging to the temperature group, while the filled circles with mango color in (b) are the site-average NEE of the sites in the prototype D-group with very high posterior probability (>99%) belonging to the dryness group (see the Methods section and supplementary table S1 available at stacks.iop.org/ERL/5/034007/mmedia). The thick green lines represent model predictions. 7 Environ. Res. Lett. 5 (2010) 034007 C Yi et al Figure 3. The site-averaged NEE of B-group sites that are sensitive to both: (a) temperature and (b) dryness. temperate and boreal climate (76% are located above 45◦ N, supplementary figure S2(a) available at stacks.iop.org/ERL/ 5/034007/mmedia), while most sites of the dryness-limited group were located in the zones of subtropical climate (63% are located below 45◦ N, supplementary figure S2(b) available at stacks.iop.org/ERL/5/034007/mmedia). The Bgroup sites were almost symmetrically distributed around 45◦ N (supplementary figure S2(c) available at stacks.iop.org/ERL/ 5/034007/mmedia). The controlling function of temperature for terrestrial carbon exchanges breaks down as mean annual temperature approaches 16 ◦ C. All sites with mean annual temperature above 16 ◦ C are in the dryness group (figures 2(a) and 3(a)). Our findings suggest that NEE at mid-to-high latitudes is controlled largely by the mean annual temperature, while at mid-to-low latitudes, it is controlled largely by dryness. The geographic region around 45◦ N is a transition zone where many sites are co-limited by both temperature and dryness. The global empirical patterns of NEE driven by climate gradients found in this paper are partially supported by another global data analysis conducted by Nemani et al (2003) based on correlation between 18 years climate data and net primary production (NPP) derived from spatially continuous satellite data. This modeling study found that NPP is largely controlled by temperature at mid-to-high latitudes, while at subtropical and tropical it is controlled by radiation and water, i.e. by dryness (ratio of net radiation to precipitation) as was used in our analysis. Even though the predicted ecological variables used here (NEE) and in Nemani et al (2003)’s analysis (NPP = NEE − soil respiration) were not the same, the consistent climate-driven spatial patterns derived from the two independently global datasets at least indicates that climate control plays an important role in the terrestrial carbon cycle. On the other hand, it is noted that our findings are different from the individual site analyses on climate control of NEE (e.g. Dunn et al 2007). These analyses study the temporal variability of NEE based on the measurements from a single site, i.e. how climate factors drive NEE changes from year to year. Our analysis studies the spatial variability of NEE based on measurements from many sites, i.e. how spatial gradients of climate drive NEE changes from location to location. Our data analysis demonstrated that spatial variability of NEE is 2.5 times greater than temporal variability of NEE (see discussion in section 3 of supplementary materials available at stacks.iop.org/ERL/5/034007/mmedia). Therefore, the existing differences between temporal variability and spatial variability are expected. Why is the average annual temperature the main climate driver of NEE at mid- and high-latitudes? The most likely reason is that higher average annual temperature also reflects prolonged growing seasons in cold climate regions and hence increases carbon uptake in biomass (White et al 1999, Malhi 2002, Kato and Tang 2008) relative to heterotrophic decomposition. At many sites, respiration rates lag NPP rates proportionally after disturbance, and a larger NPP resulting from a longer growing season contributes to higher uptake (Goulden et al 1996, Leuning et al 2005). In the absence of other factors, we therefore expect higher carbon uptake at warmer sites within the temperature group. This speculation is partially supported by previous studies with limited data (Goulden et al 1996, Leuning et al 2005). In warm climate regions (low-latitudes), growing season length is less likely to be affected by temperature variations because these regions either experience a year-round growing season or a growing season that is limited by factors other than temperature, mainly water stress. The global-biome-climate data analysis (Zhou et al 2008) indicates that the mean annual temperature of C4 grassland biome is about 23 ◦ C, in other words it is much larger than the threshold value of 16 ◦ C, and hence C4 sites are much more likely to be in a dryness group according to our findings above. It is well known in physiology that the assimilation of C4 ecosystems, which resides mainly in the subtropical regions (Ehleringer et al 2005), is independent of temperature but is limited by water stress (Lambers et al 1998). This fact partially supports our findings that the NEE-driver of a site with mean annual temperature larger than 16 ◦ C is likely to be dryness and such sites are likely located in tropical or subtropical regions. The majority of the 125 sites are recovering from past disturbance rather than being actively disturbed, and thus are in the ‘slow in’ instead of the ‘rapid out’ phase of carbon flow in the terrestrial biosphere as conceptualized by Korner (2003). Disturbance history and stand age play a large role in NEE variability (Amiro et al 2010), which is seen at chronosequence sites with similar climates (Ryan and Law 2005). Though 8 Environ. Res. Lett. 5 (2010) 034007 C Yi et al Ministry of Education, Science, Sports and Culture (MESSC). The Brazilian site is supported by the Brazilian Ministry of Science and Technology (MCT) and the LBA program. We thank the numerous scientists, students, and technicians responsible for the day-to-day gathering of the flux data, and the agency representatives who fund the respective projects. Without the dedicated efforts of so many individuals, this analysis would be impossible. the temperature and dryness groups are correlated well with their respective indices, the overlap of the two groups in temperature–dryness space suggests that NEE is controlled by a complex interaction of climate and non-climate factors. Our results do not support the recent suggestion that a single abiotic factor such as nitrogen supply dominates NEE (Magnani et al 2007, Sutton et al 2008). Links between terrestrial CO2 exchanges and climate controls are clearly demonstrated by many site-years of data from the eddy-flux tower networks. Our findings are essential to understand how future climate change may affect terrestrial CO2 exchanges with the atmosphere in the 21st century (Qian et al 2010). In the IPCC 2007 report, projected warming in the 21st century is expected to be greatest over land and at high northern latitudes, while projected decreases in precipitation are likely in most subtropical land regions (IPCC 2007). Although climate controls on long-term changes in NEE may be different from controls on spatial variability of NEE, our results imply that the most likely future climate change scenarios could strongly intensify terrestrial CO2 uptake in high-latitudes and weaken CO2 uptake in low-latitudes. References Amiro B D et al 2010 Ecosystem carbon dioxide fluxes after disturbance in forests of North America J. Geophys. 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This work was based on the database produced by the La Thuile FLUXNET project, which received financial support of CarboEuropeIP, FAO-GTOS-TCO, iLEAPS, Max Planck Institute for Biogeochemistry, National Science Foundation, University of Tuscia, US Department of Energy. We acknowledge database and technical support from Berkeley Water Center, Lawrence Berkeley National Laboratory, Microsoft Research eScience, Oak Ridge National Laboratory, University of California—Berkeley, University of Virginia. The following regional networks were involved with this work: AmeriFlux, CarboEuropeIP, Fluxnet-Canada, ChinaFlux, OzFlux, CarboAfrica, and AsiaFlux. AmeriFlux, is sponsored by the United States Departments of Energy (Terrestrial Carbon Program, National Institutes of Global Environmental Change (NIGEC), National Institute for Climate Change Research (NICCR)), Department of Commerce (NOAA), and Department of Agriculture (USDA/Forest Service), NASA, and the National Science Foundation. European sites in the NitroEurope, Euroflux and Medeflu projects are supported by the European Commission Directorate General XII Environment, Climate Program and the Program CONSOLIDER-INGENIO 2010 (GRACCIE). Canadian sites are sponsored by the Canadian Foundation for Climate and Atmospheric Sciences (CFCAS), the Natural Sciences and Engineering Research Council (NSERC), and BIOCAP Canada (Fluxnet-Canada only). Chinese sites are supported by the Chinese Academy of Sciences and the Ministry of Science and Technology. Australian sites are supported by the Australian Research Council. The Japanese site is supported by the Ministry of Agriculture, Forest and Fisheries (MAFF), the Ministry of Industrial Trade and Industry (MITI), and 9 Environ. Res. Lett. 5 (2010) 034007 C Yi et al Reichstein M et al 2007 Determinants of terrestrial ecosystem carbon balance inferred from European eddy covariance flux sites Geophys. Res. 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Change Biol. 11 1–16 10 SUPPLEMENTARY DATA Climate control of terrestrial carbon exchange across biomes and continents Chuixiang Yi1, Daniel Ricciuto2, Runze Li3, John Wolbeck1, Xiyan Xu1, Mats Nilsson4, Luis Aires5, John D Albertson6, Christof Ammann7, M Altaf Arain8, Alessandro C de Araujo9, Marc Aubinet10, Mika Aurela11, Zoltán Barcza12, Alan Barr13, Paul Berbigier14, Jason Beringer15, Christian Bernhofer16, Andrew T Black17, Paul V Bolstad18, Fred C Bosveld19, Mark S J Broadmeadow20, Nina Buchmann21, Sean P Burns22, Pierre Cellier23, Jingming Chen24, Jiquan Chen25, Philippe Ciais26, Robert Clement27, Bruce D Cook28, Peter S Curtis29, D Bryan Dail30, Ebba Dellwik31, Nicolas Delpierre32, Ankur R Desai33, Sabina Dore34, Danilo Dragoni35, Bert G Drake36, Eric Dufrêne32, Allison Dunn37, Jan Elbers38, Werner Eugster21, Matthias Falk39, Christian Feigenwinter40, Lawrence B Flanagan41, Thomas Foken42, John Frank43, Juerg Fuhrer7, Damiano Gianelle44, Allen Goldstein45, Mike Goulden46, Andre Granier47, Thomas Grünwald48, Lianhong Gu2, Haiqiang Guo49, Albin Hammerle50, Shijie Han51, Niall P Hanan52, László Haszpra53, Bernard Heinesch10, Carole Helfter54, Dimmie Hendriks55, Lindsay B Hutley56, Andreas Ibrom57, Cor Jacobs38, Torbjörn Johansson58, Marjan Jongen59, Gabriel Katul60, Gerard Kiely61, Katja Klumpp62, Alexander Knohl21, Thomas Kolb34, Werner L Kutsch63, Peter Lafleur64, Tuomas Laurila11, Ray Leuning65, Anders Lindroth58, Heping Liu66, Benjamin Loubet23, Giovanni Manca67, Michal Marek68, Hank A Margolis69, Timothy A Martin70, William J Massman43, Roser Matamala71, Giorgio Matteucci72, Harry McCaughey73, Lutz Merbold74, Tilden Meyers75, Mirco Migliavacca76, Franco Miglietta77, Laurent Misson78,117, Meelis Mölder58, John Moncrieff27, Russell K Monson79, Leonardo Montagnani80,81, Mario Montes-Helu34, Eddy Moors82,Christine Moureaux10,83, Mukufute M Mukelabai84, J William Munger85, May Myklebust65, Zoltán Nagy86, Asko Noormets87, Walter Oechel88, Ram Oren89, Stephen G Pallardy90, Kyaw Tha Paw U39, João S Pereira59, Kim Pilegaard57, Krisztina Pintér86, Casimiro Pio91, Gabriel Pita92, Thomas L Powell93, Serge Rambal94, James T Randerson46, Celso von Randow95, Corinna Rebmann64, Janne Rinne96, Federica Rossi77, Nigel Roulet97, Ronald J Ryel98, Jorgen Sagerfors4, Nobuko Saigusa99, María José Sanz100, Giuseppe-Scarascia Mugnozza101, Hans Peter Schmid102, Guenther Seufert103, Mario Siqueira89, Jean-François Soussana62, Gregory Starr104, Mark A Sutton105, John Tenhunen106, Zoltán Tuba86,117, Juha-Pekka Tuovinen11, Riccardo Valentini107, Christoph S Vogel108, Jingxin Wang109, Shaoqiang Wang110, Weiguo Wang111, Lisa R Welp112, Xuefa Wen110, Sonia Wharton113, Matthew Wilkinson20, Christopher A Williams114, Georg Wohlfahrt50, Susumu Yamamoto115, Guirui Yu110, Roberto Zampedri44, Bin Zhao49 and Xinquan Zhao116 1 School of Earth and Environmental Sciences, Queens College, City University of New York, New York 11367, USA 2 Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831 USA 3 Department of Statistics, Pennsylvania State University, University Park, Pennsylvania 16802, USA 4 Department of Forest Ecology, The Swedish University of Agricultural Sciences, SE-901 83 Umeå, Sweden 5 CESAM and Department of Environmental Engineering, School of Technology and Management, Polytechnic Institute of Leiria, Portugal 6 Department of Civil and Environmental Engineering, Duke University, Durham, North Carolina 227080287, USA 7 Federal Research Station Agroscope Reckenholz-Tänikon, Reckenholzstr. 191, 8046 Zürich, Switzerland 8 School of Geography and Earth Sciences, McMaster University, Hamilton, ON, L8S 4K1, Canada 9 Instituto Nacional de Pesquisas da Amazonia , Programa LBA, Campus-II, Manaus—Amazonas 69060, Brazil 10 University of Liege, Gembloux Agro-Bio Tech, Unit of Biosystem Physics, 2 Passage des Déportés, 5030 Gembloux, Belgium 11 Finnish Meteorological Institute, Climate Change Research, FI-00101 Helsinki, Finland 12 Department of Meteorology, Eötvös Loránd University, H-1117 Budapest, Pázmány sétány 1/A, Hungary 13 Climate Research Division, Environment Canada, Saskatoon, SK, S7N 3H5, Canada 14 INRA, UR1263 EPHYSE, Villenave d’Ornon F-33883, France 15 School of Geography and Environmental Science, Monash University, Clayton, Victoria, 3800 Australia 16 Institute of Hydrology and Meteorology, Dresden University of Technology, Pienner Str. 23, D-01737 Tharandt, Germany 17 Land and Food Systems, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada 18 University of Minnesota, 115 Green Hall 1530 Cleveland Avenue N St Paul, Minnesota, 55108, USA 19 Royal Netherlands Meteorological Institute, 3730 AE De Bilt, The Netherlands 20 Forest Research, Alice Holt Lodge, Farnham, Surrey, GU10 4LH, UK 21 ETH, Zurich, Institute of Plant Science, Universitaetsstrasse 2, Zuerich 8092, Switzerland 22 National Center for Atmospheric Research Boulder, CO 80307-3000, USA 23 UMR INRA-INA PG—Environment & Arable Crops Unit 78850 Thiverval-Grignon, France 24 Department of Geography, University of Toronto, Toronto, ON, M5S 3G3, Canada 25 Department of Environmental Sciences, University of Toledo, Toledo, OH 43606-3390, USA 26 LSCE, UMR CEA-CNRS, Batiment 709, CE, L’Orme des Merisiers, F-91191 Gif-sur-Yvette, France 27 School of GeoSciences, The University of Edinburgh, Mayfield Road, Edinburgh, EH9 3JU, UK 28 Biospheric Sciences Branch, NASA’s Goddard Space Flight Center, Greenbelt, MD 20771, USA 29 Department of Evolution, Ecology, and Organismal Biology, Ohio State University, Columbus, OH 43210, USA 30 Department of Plant, Soil, and Environmental Science, University of Maine, Orono, ME 04469, USA 31 Wind Energy Division, Risø National Laboratory for sustainable Energy, Technical University of Denmark, PO 49, DK-4000 Roskilde, Denmark 32 Université Paris-Sud Bâtiment 362, Ecologie, Systematique et Evolution Orsay Cedex, F-91405, France 33 Department of Atmospheric and Oceanic Sciences, University of Wisconsin, Madison, WI 53706, USA 34 Northern Arizona University, School of Forestry Northern Arizona University, Flagstaff, AZ 86001, USA 35 Atmospheric Science Program, Department of Geography, Indiana University, Bloomington, IN 47405, USA 36 Smithsonian Environmental Research Center, Edgewater, MD 21037, USA 37 Department of Physical and Earth Science, Worcester State College, 486 Chandler Street Worcester, MA 01602, USA 38 ESS-CC, Alterra Wageningen UR, 6700 AA Wageningen, The Netherlands 39 Atmospheric Science Group, LAWR, UC Davis, Davis, CA 95616, USA 40 Institute for Meteorology, Climatology and Remote Sensing, University of Basel, Klingelbergstrasse 27, CH-4056 Basel, Switzerland 41 Department of Biological Sciences, University of Lethbridge, 4401 University Drive, Lethbridge, Alberta, T1K 3M4, Canada 42 Department of Micrometeorology, University of Bayreuth, 95440 Bayreuth, Germany 43 USDA Forest Service, Rocky Mountain Research Station, 240 West Prospect, Fort Collins, CO 80526, USA 44 IASMA Research and Innovation Centre, Fondazione E Mach, Environment and Natural Resources Area, San Michele all’Adige, I38010 Trento, Italy 45 Department of Environmental Science, Policy and Management, University of California, Berkeley, CA 94720, USA 46 Department of Earth System Science, University of California, Irvine, CA 92697, USA 47 INRA, UMR 1137 Ecologie et écophysiologie Forestierès, F54280, Champenoux, France Technische Universität Dresden, Institute of Hydrology and Meteorology, Department of Meteorology, Piennerstrasse 9, 01737 Tharandtt, Germany 49 Ministry of Education Key Laboratory for Biodiversity Science and Ecological Engineering, Institute of Biodiversity Science, Fudan University, Shanghai 200433, People’s Republic of China 50 University of Innsbruck, Institute of Ecology Sternwartestr 15, Innsbruck 6020, Austria 51 Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, People’s Republic of China 52 Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO 80523, USA 53 Hungarian Meteorological Service, H-1675 Budapest, PO Box 39, Hungary 54 Centre for Ecology and Hydrology (Edinburgh) Bush Estate Penicuik, Midlothian, EH26 0QB, UK 55 Department of Hydrology and Geo-Environmental Sciences, Boelelaan 1085, 1081 HV, VU University Amsterdam, The Netherlands 56 School of Environmental and Life Sciences, Charles Darwin University, Darwin, NT 0909, Australia 57 Biosystems Division, Risø National Laboratory for Sustainable Energy, Technical University of Denmark, PO 49, DK-4000 Roskilde, Denmark 58 Geobiosphere Science Centre, Physical Geography and Ecosystems Analysis, Lund University, Sölvegatan 12, SE-223 62 Lund, Sweden 59 Instituto Superior de Agronomia, Universidade Técnica de Lisboa, Tapada da Ajuda 1349-017 Lisboa, Portugal 60 School of the Environment, Duke University, Durham, NC 27708-0328, USA 61 Civil and Environmental Engineering Department, University College Cork, Cork, Republic of Ireland 62 INRA, Unité d’Agronomie, 234, Avenue du Brézet, F-63000 Clermont-Ferrand, France 63 Johann Heinrich von Thünen-Institut (vTI), Institut für Agrarrelevante Klimaforschung, Bundesallee 50, 38116 Braunschweig, Germany 64 Department of Geography, Trent University, Peterborough, Ontario, K9J 7B8, Canada 65 CSIRO Marine and Atmospheric Research, PO Box 3023, Canberra, ACT, 2601, Australia 66 Department of Physics, Atmospheric Sciences & Geoscience, Jackson State University, Jackson, MS 39217, USA 67 Rende Division, Institute for Atmospheric Pollution, Consiglio Nazionale delle Ricerche, 87036 Rende, Italy 68 Institute of Systems Biology and Ecology, Division of Ecosystems Processes Lab. of Plants Ecological Physiology, Na Sadkach 7 370 050 Ceske Budejovice Czech Republic 69 Centre d’études de la forêt Faculté de Foresterie et de Géomatique, Université Laval, Québec G1V 0A6, Canada 70 University of Florida, Gainesville, FL 32611, USA 71 Argonne National Laboratory, Biosciences Division, Argonne, IL 60439, USA 72 National Research Council, Institute of Agroenvironmental and Forest Biology, 00015 Monterotondo Scalo (RM), Italy 73 Department of Geography, Queen’s University, Kingston, Ontario, K7L 3N6, Canada 74 Max-Planck Institute for Biogeochemie, Jena, D-07745, Germany 75 NOAA/ATDD, Oak Ridge, TN 37831-2456, USA 76 Remote Sensing of Environmental Dynamics Laboratory, DISAT, Università degli Studi di MilanoBicocca, Italy 77 CNR-IBIMET, Istituto di Biometeorologia, via Giovanni Caproni 8, 50145 Firenze Italy 78 CNRS-CEFE, 1919 route de Mende, 34293 Montpellier Cedex 5, France 79 Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, CO 80309, USA 80 Servizi Forestali, Agenzia per l’Ambiente, Provincia Autonoma di Bolzano, 39100 Bolzano, Italy 81 Faculty of Sciences and Technologies, Free University of Bozen-Bolzano, Piazza Università 1, 39100, Bolzano, Italy 82 Alterra Green World Research, Wageningen, NL 6700 AA, The Netherlands 83 University of Liege, Gembloux Agro-Bio Tech, Unit of Crops Management, 2 Passage des Déportés, 5030 Gembloux, Belgium 84 Zambian Meteorological Department, Western Province, Mongu, Zambia 85 Division of Engineering and Applied Science, Department of Earth and Planetary Science, Harvard University, Cambridge, MA 02138, USA 86 Institute of Botany and Ecophysiology, Agricultural University of Gödöllô, H-2103 Gödöllô, Páter Károly u. 1, Hungary 48 87 Department of Forestry and Environmental Resources, North Carolina State University, NC 29695, USA 88 Department of Biology, San Diego State University, San Diego, CA 92182-4614, USA 89 Nicholas School of the Environment and Earth Sciences, Duke University, Durham, NC 27708-0328, USA 90 Department of Forestry, University of Missouri, Columbia, Missouri 65211, USA 91 CESAM and Department of Environment, University of Aveiro, Aveiro 3810-193, Portugal 92 Instituto Superior Tecnico, Mechanical Engineering Department, 1049-001 Lisboa, Portugal 93 The Department of Organismic and Evolutinary Biology, Harvard University, Cambridge, MA 02138, USA 94 DREAM, CEFE, CNRS, UMR5175, 1919 route de Mende, F-34293 Montpellier Cedex 5, France 95 Earth System Science Center, National Institute of Space Research, Cachoeira Paulista, SP 12630, Brazil 96 Department of Physics, FI-00014, University of Helsinki, Finland 97 Department of Geography, McGill University 805, Sherbrooke Street West Montréal, Québec, H3A 2K6, Canada 98 Department of Wildland Resources, Utah State University, Logan, UT 84322-5230, USA 99 Center for Global Environmental Research, National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba 305-8506, Japan 100 Centro de Estudios Ambientales del Mediterraneo, Parque Tecnologico, Charles H Darwin 14, E46980 Paterna, Spain 101 Agricultural Research Council, Department of Agronomy, Forestry and Land Use, 00184, Rome, Italy 102 Atmospheric Environmental Research Institute of Meteorology and Climate Research, Forschungszentrum Karlsruhe, Garmisch-Partenkirchen, Germany 103 Institute for Environment and Sustainability, Joint Research Center European Commission, TP 280, I21020 Ispra, Italy 104 Department of Biological Sciences, University of Alabama, Tuscaloosa, AL 35487-0206 USA 105 Atmospheric Sciences Centre for Ecology and Hydrology (CEH), Bush Estate, Penicuik, Midlothian, EH26 0QB, UK 106 Department of Plant Ecology, University of Bayreuth, 95440 Bayreuth, Germany 107 Department of Forest Environment and Resources, University of Tuscia, I-01100 Viterbo, Italy 108 The University of Michigan Biological Station, Pellston, MI 49769, USA 109 School of Mathematics, Liaoning Normal University, Dalian 116039, People’s Republic of China 110 Institute of Geographic Sciences and Natural Resource Research, Chinese Academy of Science, Beijing 100101, People’s Republic of China 111 IMSG@National Center for Environmental Predictions, NOAA, Camp Springs, MD 20746, USA 112 Geosciences Research Division, Scripps Institution of Oceanography, University of California, La Jolla, CA 92093, USA 113 Atmospheric, Earth and Energy Division, Lawrence Livermore National Laboratory, Livermore, CA 94551, USA 114 Graduate Degree Program in Geography, Clark University, Worcester, MA 01610-1477, USA 115 Okayama University, Okayama 700-8530, Japan 116 Northwest Plateau Institute of Biology, Chinese Academy of Sciences, Xining 810001 Qinghai, People’s Republic of China 117 Deceased. The authors from seventh to the end are listed alphabetically and contributed equally to this work. Methods 1. Meteorological data gap filling Producing reliable estimates of site-average temperature, radiation and precipitation requires comprehensive gap-filling techniques because of the sporadic data collection outages that occur at eddy covariance sites. Without gap filling, the distribution of these gaps can bias long-term averages (e.g., if there are more gaps in summer, the site’s mean temperature will have a low bias). Although gap-filled meteorological data are available from the FLUXNET database, these are problematic because they do not account for missing precipitation data. We developed an algorithm to locate the nearest flux tower or climate station in the National Climatic Data Center (NCDC in Asheville North Carolina) database to provide daily temperature and precipitation data. If data from a nearby tower were available, these were used to fill missing meteorological data. When alternate towers were not available within a 30 km radius, daily NCDC data from the nearest station were downscaled to hourly or half-hourly resolution and used to fill the gaps. Temperature data were downscaled by using the daily maximum and minimum information to construct a sine wave with the appropriate amplitude (assuming daily maximum at 15 LST and daily minimum at 3 LST), and precipitation data were downscaled by dividing daily totals by the number of daily time steps (24 or 48 depending on the site). Differences in annual averages between the eddy covariance site and the climate stations were adjusted using linear regression so that the inclusion of station data did not alter long-term temperature or precipitation averages. Net radiation data were not available from NCDC. If no alternate tower was available, gaps in these data were filled with the diurnal average values for the given hour and day of year. Diurnal averages were calculated for each hour or half-hour and day of the year using all available years and a 20day moving window. Similarly, if NCDC temperature and precipitation data were not available to fill data gaps, diurnal average values of the site were also used. The accuracy of our empirical findings are limited by eddy flux measurements in the following aspects: (1) the flux sites probably do not represent true random samples of biome types; a number of biomes, like tropical rain forests and savannas, are underrepresented; and (2) potential biases exist in the eddy covariance method as a result of advection errors, energy imbalance errors, and errors associated with the data integration approach. 2. Segregation method 2.1 Posterior probability and prototype subgroups We first employed mixtures of a third-order polynomial regression (Goldfeld and Quandt 1976) with two subpopulations, one for a temperature-limited group (TG) and the other for a dryness-limited group (DG). The mixtures regression provides us the posterior probabilities of each site belonging to TG and DG. Supplementary table S1 lists the posterior probability of each site belonging to the temperature limited group PP(TG) and to the dryness limited group PP(DG). From this table the initial temperature and dryness limited prototypes can be defined based on probability of belonging to a specific group. Using only sites that have a larger than 99% probability of belonging either to the T Group or to the D Group, a set of prototype subgroups can be selected. From the 125 site population 26 sites meet this objective criteria of being highly temperature limited and 21 sites meet the criteria for being highly dryness limited (the filled circles with mango colour in figures 2(a) and 2(b), respectively. Also see supplementary table S1). Analysis of the 26 highly temperature limited sites (>99% confidence) and the 21 highly dryness limited sites (>99% confidence) allows a set of prototype equations to be developed, which will predict the NEE of any site based on their mean annual temperature or their dryness. These two prototype equations are: NEET = −0.001T 3 − 0.0143T 2 + 0.0271T + 0.2399, (1) NEED = −0.5726 D3 + 0.7323D 2 + 5.7007 D − 9.9968 , (2) where T is mean annual temperature in oC, D = Rn/( λ P) is dryness, Rn is mean annual net radiation MJ m yr , P is mean annual precipitation mm yr , and λ (=2.5 MJ kg ) is the enthalpy of vaporization, NEET and NEED are the site-average NEE predicted by the prototype model (1) and (2). -2 -1 -1 -1 2.2 Residual index Having defined the criteria equations for temperature and dryness prediction of net ecosystem exchange of carbon, a further statistical analysis of the residual error between the predicted and observed NEE values can be performed. From this residual error analysis, a dimensionless residual index (RI) is given by: RI = RE D − RET , RE D + RET (3) where RED = ( NEED − NEEO ) / NEEO × 100% is a percent error in equation (2) for a site, NEEO is the observed mean annual NEE at the site, NEED prediction by the RET = ( NEET − NEEO ) / NEEO × 100% is a percent error in NEET prediction by the equation (1) for the site. The values of RI were calculated for all the 125 sites and listed in supplementary table S1. 2.3 Grouping by the residual index The residual index value (RI) is useful in the classification of different response functions of ecosystem carbon exchanges. A positive RI indicates a temperature-limited site while a negative RI indicates a dryness-limited site. However, how shall we interpret sites that have a RI value near zero? A low RI value indicates that the predictive ability of the NEET and NEED equations each have similar outcomes. Sites with a low RI appear to be equally limited by both mean annual temperature and dryness. Given this result we can classify a third type of sub group called the B group since they are approximately equally sensitive to both of the meteorological parameters of temperature and dryness. The B-group sites are defined with RI values between +30% and -30% (figure 3, supplementary table S1). The monotonic function of the T-group with temperature and the D-group with dryness are cross-verified by an independent nonparametric analysis (figure 1), as well as the bi-variable function of the B-group with temperature and dryness (details see the text). 3. Sensitivity analysis Lengths (durations) of site data sets are different (supplementary table S1). To test the potential influence of different data set length on the results, we conducted sensitivity analysis in five cases: (1) removing all data that were before 2000; (2) removing all single-year sites; (3) removing all sites with less than three years of data; (4) removing all sites with less than four years of data; and (5) removing all sites with less than five years of data. The sensitivity analysis indicates that the relationships between siteaverage NEE and climate controls found in this paper are stable to the perturbation of difference of sampling years. This result raises the question why the results are insensitive to temporal perturbations. To answer this question, we conducted a comparison analysis between spatial and temporal variability of NEE. The fundamental reason for the relative insensitivity to variation in length of the data sets is that spatial variability of NEE is 2.5 times greater than temporal variability of NEE. Mixture regression Here we explain why the commonly used clustering methods, including K-mean, multiple discriminate analysis, mixture models, may not work well here. These methods cluster or partition the sample space of (T, D, NEE), where T stands for temperature, D for dryness. As an illustration under what condition the commonly used cluster methods work , we generate a random sample of size 200 from a mixture of normal distribution (x,y), 50% sample from bivariate normal distribution with mean (-1.5,0) and covariance matrix being a diagonal matrix with diagonal elements 1 and 1, and 50% sample from bivariate normal distribution with mean (1.5,0) and covariance matrix being a diagonal matrix with diagonal elements 1 and 1. The scatter plot of the generated sample is depicted in supplementary figure S3. The vertical line x=0 is the theoretic optimal line to partition the sample space into two parts with a certain misclassification rate. If the purpose is to partition the sample space, then one should be clustering methods to group data. It is worth to noting that what we are interested in is the regression relation between (T,D) and NEE, and what we want to do is to group the data by the regression function of NEE on (T,D) rather than partition the sample space into some several subspaces. Therefore, we conduct mixture regression, which is different from a mixture model in that the mixture regression is to group data by taking into account the regression relation between response and predictors. As an illustration under what conditions the mixture regression may work better than the commonly used clustering method, we generate a random sample of size 200 from a mixture regression model: with 50% probability, y= x + e, where the random error e follows N(0,1); with 50% probability, y= -x + e, where the random error e follows N(0,1). The scatter plot of (x,y) is depicted in supplementary figure S4, from which it is easy to imagine that most clustering method won’t be able to partition the sample space into two parts with low misclassification rate, while mixture regression can be used to identify the two different regression relations. Uncertainties in NEE gap-filling Uncertainty about the u* threshold is the largest contributor, with annual uncertainties between 0.15 and 1.00 t C ha-1 yr-1. The 90% confidence interval generally ranges between 10-20% of annual NEE. Gap-filling uncertainties can be estimated by comparing results from different methodologies. Based on a survey of 18 different gap filling methods, Moffat et al (2007) concluded that most methods produced estimates of annual integrated NEE that were within 0.25 t C ha-1 y-1 of the mean of the other methods. Accumulated random error caused by turbulent sampling uncertainties are around 0.20 t C ha-1 yr-1 at Howland forest (Richardson et al 2006), and are expected to be of similar magnitude at other sites with some variation caused by differences in micrometeorological conditions (Wohlfahrt et al 2008a, 2008b). Total errors in annual estimates of NEE typically range between 0.3 and 1 t C ha-1 yr-1. The total error is certainly below the value of 2 t C ha-1 yr-1 tested conservatively by a Monte-Carlo analysis. For the purpose of this study, we conservatively estimate the 90% confidence interval of site NEE by adding the three major sources of error in quadrature, assuming that the sources of error are independent and that u* uncertainty is 20% of annual NEE: σ NEE (t C ha -1 yr -1 ) = 0.202 + 0.252 + (0.2* NEEav )2 where NEEav is the site average NEE. We conclude that these errors do not significantly affect the outcome of our analysis because the spatial variability in NEE among sites is much larger than the random error. References Goldfeld S M and Quandt R E 1976 A Markov model for switching regression J. Econom. 1 3–16 Moffat A M et al 2007 Comprehensive comparison of gap filling techniques for eddy covariance net carbon fluxes Agric. Forest Meteorol. 147 209–32 Richardson A D et al 2006 A multi-site analysis of random error in tower-based measurements of carbon and energy fluxes Agric. Forest Meteorol. 136 1–18 Wohlfahrt G, Fenstermaker L F and Arnone J A III 2008a Large annual net ecosystem CO2 uptake of a Mojave Desert ecosystem Glob. Change Biol. 14 1475–87 Wohlfahrt G et al 2008b Seasonal and inter-annual variability of the net ecosystem CO2 exchange of a temperate mountain grassland: effects of weather and management J. Geophys. Res. 113 D08110 Supplementary table S1. Main site characteristics, climatic index, posterior probability, residual index, group classification, and carbon flux of terrestrial ecosystems observed in this analysis. Latitude Site Code o ( N) Longitude o ( E) Elevation Vegetation o T ( C) (m) C‐flux Dryness type (t C ha‐1 yr‐1) PP (TG) PP (DG) RI Group Years of data US‐Atq 70.47 ‐157.41 15 WET ‐10.60 4.87 ‐0.45 100.0% 0.0% 100% TG 2003‐2006 IE‐Dri 51.99 ‐8.75 187 GRA 9.64 0.51 ‐1.85 99.9% 0.1% 95% TG 2003 CA‐Mer 45.41 ‐75.52 70 WET 6.21 1.05 ‐0.53 98.0% 2.0% 92% TG 1999‐2006 IT‐Cpz 41.71 12.38 68 EBF 14.90 1.68 ‐5.60 100.0% 0.0% 90% TG 1997, 2001‐2006 CA‐NS4 55.91 ‐98.38 260 ENF ‐2.08 1.56 0.05 77.9% 22.1% 90% TG 2003‐2004 CA‐NS7 56.64 ‐99.95 273 OSH ‐1.70 1.41 0.29 92.2% 7.8% 89% TG 2003‐2004 IT‐MBo 46.02 11.05 1550 GRA 5.65 0.97 ‐0.47 99.4% 0.6% 89% TG 2003 AT‐Neu* 47.12 11.32 970 GRA 6.50 0.67 ‐0.10 100.0% 0.0% 88% TG 2001‐2008 FI‐Kaa 69.14 27.30 155 WET ‐1.10 0.64 ‐0.20 100.0% 0.0% 88% TG 2000‐2007 CA‐TP4 42.71 ‐80.36 184 ENF 8.55 1.08 ‐1.36 88.2% 11.8% 87% TG 2003‐2007 FI‐Sod* 67.36 26.64 180 ENF ‐0.70 0.80 0.62 100.0% 0.0% 87% TG 2000‐2001, 2003‐2007 IT‐PT1 45.20 9.06 60 DBF 14.27 1.82 ‐4.86 99.9% 0.1% 85% TG 2003 US‐WBW* 35.96 ‐84.29 283 DBF 14.92 0.95 ‐5.74 72.7% 27.3% 84% TG 1995‐1998 DK‐Sor 55.49 11.65 40 DBF 8.25 0.75 ‐0.63 99.9% 0.1% 83% TG 1997‐2006 US‐Wrc* 45.82 ‐121.95 371 ENF 8.92 0.54 ‐0.79 100.0% 0.0% 83% TG 1999‐2002, 2004 FR‐Lq1 45.64 2.74 1040 GRA 7.66 0.32 ‐1.51 100.0% 0.0% 82% TG 2004‐2006 IT‐SRo 43.73 10.28 4 ENF 14.20 1.59 ‐4.76 99.1% 0.9% 81% TG 1999‐2007 SE‐Deg* 64.18 19.55 270 WET 2.56 0.45 ‐0.53 100.0% 0.0% 81% TG 2001‐2002, 2004‐2005 US‐Ivo 68.49 ‐155.75 570 WET ‐9.37 1.38 ‐0.22 86.1% 13.9% 80% TG 2004‐2006 DE‐Bay* 50.14 11.87 775 ENF 6.20 0.64 0.44 100.0% 0.0% 78% TG 1997‐1999 CA‐Qfo* 49.69 ‐74.34 382 ENF 1.11 0.97 ‐0.33 99.6% 0.4% 74% TG 2004‐2006 FR‐Lq2 45.64 2.74 1040 GRA 7.66 0.32 ‐1.86 100.0% 0.0% 72% TG 2004‐2006 CA‐Qcu 49.27 ‐74.04 392 ENF 1.26 0.81 1.41 100.0% 0.0% 70% TG 2002‐2006 CA‐SJ3 53.88 ‐104.64 488 ENF 2.17 2.06 0.31 59.2% 40.8% 69% TG 2005 CA‐TP1 42.66 ‐80.56 265 ENF 8.73 0.82 ‐0.38 99.8% 0.2% 68% TG 2003‐2007 CA‐Man 55.88 ‐98.48 259 ENF ‐1.23 1.91 0.09 63.4% 36.6% 68% TG 1994‐2006 IT‐Amp 41.90 13.61 884 GRA 9.52 1.20 ‐1.28 73.5% 26.5% 65% TG 2003‐2006 PT‐Esp 38.64 ‐8.60 95 EBF 16.02 2.17 ‐5.76 100.0% 0.0% 62% TG 2002‐2004, 2006‐2007 CA‐SJ2 53.94 ‐104.65 580 ENF 0.42 1.08 1.48 100.0% 0.0% 62% TG 2003‐2006 DE‐Wet* 50.45 11.46 785 ENF 6.52 0.87 ‐1.32 98.8% 1.2% 61% TG 2002‐2007 US‐FPe 48.31 ‐105.10 634 GRA 5.75 1.41 0.32 83.6% 16.4% 61% TG 2000‐2006 SE‐Abi 68.36 18.79 TBD DBF 0.10 0.42 ‐1.30 100.0% 0.0% 60% TG 2005 CA‐Ca3 49.53 ‐124.90 165 ENF 8.75 0.53 0.63 100.0% 0.0% 59% TG 2001‐2006 IT‐Non 44.69 11.09 25 DBF 13.80 1.04 ‐5.04 68.2% 31.8% 57% TG 2001‐2003, 2006 SE‐Nor 60.09 17.48 43 EBF 6.25 1.07 0.96 99.8% 0.2% 56% TG 1996‐1997, 1999, 2003, 2005 FI‐Sii* 61.83 24.19 162 WET 3.99 1.35 ‐0.51 83.7% 16.3% 53% TG 2005 IT‐Ro2 42.39 11.92 224 DBF 14.88 1.42 ‐7.52 100.0% 0.0% 52% TG 2002‐2006 CA‐NS6 55.92 ‐98.96 276 OSH ‐0.35 1.51 ‐0.23 75.8% 24.2% 51% TG 2002‐2004 US‐WCr 45.81 ‐90.08 520 DBF 5.27 1.21 ‐0.90 87.6% 12.4% 49% TG 1999‐2006 SE‐Fla 64.11 19.46 226 ENF 2.69 1.27 ‐0.57 88.8% 11.2% 48% TG 1997‐1998. 2001‐2002 DK‐Lva 55.68 12.08 15 GRA 9.33 0.77 ‐2.57 93.4% 6.6% 48% TG 2006‐2007 JP‐TAK* 36.15 137.42 1420 DBF 6.53 0.47 ‐2.28 99.8% 0.2% 46% TG 1994‐2004 US‐Syv 46.24 ‐89.35 540 MF 4.20 1.01 ‐1.16 95.8% 4.2% 42% TG 2002‐2003, 2005 US‐IB2 41.84 ‐88.24 227 GRA 10.46 2.14 ‐3.97 99.2% 0.8% 37% TG 2005 US‐PFa 45.95 ‐90.27 470 MF 4.99 1.24 ‐1.02 83.4% 16.6% 35% TG 1997‐2000, 2003 CA‐Gro 48.22 ‐82.16 300 MF 3.36 1.30 ‐0.83 81.7% 18.3% 30% TG 2004‐2006 US‐Me3* 44.32 ‐121.61 1005 ENF 8.49 2.76 ‐1.76 60.5% 39.5% 28% BG 2004‐2005 US‐Ha1* 42.54 ‐72.17 340 DBF 7.88 0.78 ‐2.53 91.7% 8.3% 28% BG 1992‐2007 FR‐LBr 44.72 ‐0.77 61 ENF 14.03 1.29 ‐4.12 77.7% 22.3% 27% BG 1997‐1998 HU‐HH2* 46.96 16.65 248 GRA 8.90 1.10 ‐2.20 73.7% 26.3% 25% BG 1999‐2000, 2007 CA‐Ojp 53.92 ‐104.69 579 ENF 1.52 1.69 ‐0.25 65.8% 34.2% 23% BG 2000‐2006 US‐NC2* 35.80 ‐76.67 12 ENF 15.80 0.94 ‐5.91 79.8% 20.2% 22% BG 2005‐2008 CA‐Let* 49.71 ‐112.94 960 GRA 6.41 2.12 ‐1.30 69.6% 30.4% 17% BG 1999‐2006 US‐MOz 38.74 ‐92.20 219 DBF 13.52 1.47 ‐3.40 74.5% 25.5% 17% BG 2005‐2006 FR‐Fon* 48.48 2.78 90 DBF 11.50 0.84 ‐3.80 63.6% 36.4% 13% BG 2006 US‐UMB* 45.56 ‐84.71 234 DBF 5.50 1.19 ‐1.51 76.8% 23.2% 11% BG 1999‐2003 US‐OHO* 41.55 ‐83.84 230 DBF 10.40 1.42 ‐2.67 64.3% 35.7% 9% BG 2004‐2008 CH‐Oe1* 47.29 7.73 450 GRA 9.57 0.65 ‐3.72 70.5% 29.5% 8% BG 2002‐2007 US‐ME4* 44.44 ‐121.57 1183 ENF 7.89 2.77 ‐2.06 59.5% 40.5% 7% BG 2001‐2002 NL‐Loo* 52.17 5.74 25 ENF 10.30 1.00 ‐3.07 65.3% 34.7% 4% BG 1997‐2007 US‐Ho1* 45.20 ‐68.74 60 ENF 6.61 1.17 ‐1.88 70.6% 29.4% ‐1% BG 1996‐2004 DE‐Hai* 51.08 10.45 430 DBF 8.31 0.89 ‐2.94 69.1% 30.9% ‐1% BG 2000‐2007 US‐MLT* 42.50 ‐113.41 1370 GRA 8.75 2.90 ‐0.26 83.1% 16.9% ‐1% BG 2005 CA‐Ca1 49.87 ‐125.33 300 ENF 8.69 0.73 ‐3.59 58.1% 41.9% ‐6% BG 1998‐2006 US‐Me2* 44.45 ‐121.56 1253 ENF 7.61 2.91 ‐4.71 5.9% 94.1% ‐7% BG 2002‐2008 AU‐Wac* ‐37.43 145.19 545 EBF 10.10 0.80 ‐3.76 57.1% 42.9% ‐8% BG 2006 CN‐Cha* 42.40 128.10 761 MF 4.80 1.90 ‐2.50 65.9% 34.1% ‐9% BG 2003‐2004 US‐Dk3* 35.98 ‐79.09 163 ENF 14.73 1.10 ‐4.54 69.4% 30.6% ‐9% BG 2001‐2005 DE‐Gri 50.95 13.51 385 GRA 7.99 0.97 ‐2.83 62.9% 37.1% ‐12% BG 2005‐2006 CN‐Do1 31.52 121.96 2‐5 WET 15.64 0.58 ‐6.23 52.3% 47.7% ‐17% BG 2005 US‐BN1* 63.92 ‐145.38 518 ENF 0.15 1.99 ‐1.40 50.6% 49.4% ‐18% BG 2002‐2004 CA‐WP1 54.95 ‐112.47 540 MF 1.87 1.85 ‐2.21 42.0% 58.0% ‐20% BG 2004‐2007 CN‐Do2 31.58 121.90 2‐5 WET 15.56 0.70 ‐4.37 60.1% 39.9% ‐21% BG 2005 CA‐SJ1 53.91 ‐104.66 580 ENF 0.68 2.08 ‐0.73 58.4% 41.6% ‐24% BG 2004‐2005 US‐Bar 44.06 ‐71.29 272 DBF 7.54 0.76 ‐3.71 30.3% 69.7% ‐24% BG 2004‐2006 CN‐HaM 37.37 101.18 3250 GRA ‐1.53 2.48 ‐0.49 57.8% 42.2% ‐25% BG 2003‐2005 IT‐Ren* 46.59 11.43 1730 ENF 4.75 1.20 ‐2.00 54.7% 45.3% ‐28% BG 1999,2001‐2007 US‐BN3* 63.92 ‐145.74 469 MF 0.15 1.99 ‐0.09 61.2% 38.8% ‐32% DG 2002‐2003 US‐Blo 38.90 ‐120.63 1315 ENF 11.23 0.99 ‐5.76 14.2% 85.8% ‐33% DG 2000‐2006 US‐MMS 39.32 ‐86.41 275 DBF 12.36 1.05 ‐4.23 58.2% 41.8% ‐33% DG 1999‐2005 US‐Dk2* 35.97 ‐79.10 168 DBF 15.06 1.07 ‐4.44 63.6% 36.4% ‐38% DG 2001‐2005 US‐Fuf* 35.09 ‐111.76 2180 ENF 9.15 2.04 ‐0.58 32.0% 68.0% ‐39% DG 2007 US‐Goo 34.25 ‐89.87 87 GRA 16.31 0.95 ‐2.13 2.1% 97.9% ‐40% DG 2003‐2006 CA‐NS2 55.91 ‐98.52 260 ENF 0.85 1.70 ‐1.91 33.3% 66.7% ‐42% DG 2002, 2004 HU‐Bug 46.69 19.60 140 GRA 9.99 1.63 ‐0.74 27.6% 72.4% ‐42% DG 2003‐2007 BE‐Vie 50.31 6.00 450 MF 8.18 1.10 ‐5.17 2.8% 97.2% ‐45% DG 1997‐2006 US‐SP3* 29.75 ‐82.16 50 ENF 20.06 1.03 ‐6.40 79.5% 20.5% ‐47% DG 2001‐2004 FR‐Hes* 48.67 7.06 300 DBF 9.99 0.97 ‐3.71 46.3% 53.7% ‐49% DG 1997‐1999, 2001‐2007 DE‐Tha* 50.96 13.57 380 ENF 8.79 0.94 ‐6.00 0.4% 99.6% ‐51% DG 1997‐2007 AU‐TUM ‐35.66 148.15 1200 EBF 9.50 1.26 ‐3.37 52.3% 47.7% ‐51% DG 2002‐2007 NL‐Hor* 52.03 5.07 ‐2.2 GRA 10.98 1.11 ‐3.29 59.3% 40.7% ‐52% DG 2004‐2005 IT‐Col 41.85 13.59 1550 DBF 7.36 0.96 ‐5.87 0.1% 99.9% ‐55% DG 1997‐1998, 2000‐2001, 2005 CA‐Oas 53.63 ‐106.20 530 DBF 2.27 1.67 ‐1.61 45.1% 54.9% ‐56% DG 1997‐2006 US‐Ton 38.43 ‐120.97 177 WSA 16.29 2.11 ‐1.71 0.2% 99.8% ‐56% DG 2002‐2006 CA‐NS1 55.88 ‐98.48 260 ENF 0.37 1.83 ‐0.94 53.3% 46.7% ‐56% DG 2004 CA‐TP3 42.71 ‐80.35 184 ENF 8.81 1.10 ‐4.42 15.1% 84.9% ‐58% DG 2003‐2007 US‐Fmf* 35.14 ‐111.73 2160 ENF 9.99 2.07 0.51 2.7% 97.3% ‐58% DG 2007 IT‐Ro1 42.41 11.93 234 DBF 15.37 1.38 ‐3.04 20.5% 79.5% ‐61% DG 2001‐2006 UK‐Gri 56.61 ‐3.80 340 ENF 7.38 0.86 ‐6.12 0.0% 100.0% ‐63% DG 1997‐1998, 2000‐2001 CN‐Do3 31.52 121.97 2‐5 WET 15.67 0.77 ‐5.12 60.0% 40.0% ‐65% DG 2005 FR‐Pue 43.74 3.60 270 EBF 13.67 1.23 ‐2.60 31.7% 68.3% ‐71% DG 2001‐2007 UK‐Ham 51.12 ‐0.86 80 DBF 10.50 0.59 ‐5.88 1.6% 98.4% ‐71% DG 2004 US‐Aud 31.59 ‐110.51 1469 GRA 16.12 1.94 0.97 0.0% 100.0% ‐72% DG 2003‐2005 CA‐NS5 55.86 ‐98.49 260 ENF ‐1.76 1.69 ‐1.25 41.7% 58.3% ‐72% DG 2002, 2004 US‐SO3 33.38 ‐116.62 1429 CSH 14.50 2.03 ‐0.89 0.2% 99.8% ‐73% DG 2005‐2006 UK‐EBu 55.87 ‐3.21 190 GRA 9.08 0.42 ‐6.73 0.0% 100.0% ‐74% DG 2004 CZ‐BK1 49.50 18.54 908 ENF 8.26 0.64 ‐7.09 0.0% 100.0% ‐74% DG 2004‐2006 PT‐Mi1 38.54 ‐8.00 250 EBF 15.86 2.46 ‐0.89 0.0% 100.0% ‐76% DG 2003‐2005 ZM‐MON* ‐15.43 23.25 1053 SAV 22.00 1.42 ‐0.01 0.0% 100.0% ‐78% DG 2007 US‐Var 38.41 ‐120.95 129 GRA 15.94 1.60 ‐0.58 0.0% 100.0% ‐79% DG 2001‐2006 ES‐LMa 39.94 ‐5.77 260 SAV 16.16 1.46 ‐1.28 0.0% 100.0% ‐82% DG 2004‐2006 US‐GLE* 41.36 ‐106.24 3190 ENF 0.09 0.97 ‐3.90 0.2% 99.8% ‐83% DG 2005‐2008 US‐NR1 40.03 ‐105.55 3050 ENF 2.46 1.86 ‐0.49 60.5% 39.5% ‐83% DG 1999‐2000, 2002‐2003 CA‐NS3 55.91 ‐98.38 260 ENF ‐2.43 1.71 ‐0.89 49.9% 50.1% ‐85% DG 2002‐2004 US‐KS2 28.61 ‐80.67 3 CSH 22.11 1.31 ‐3.60 0.0% 100.0% ‐85% DG 2002, 2004‐2006 BR‐Ma2* ‐2.61 ‐60.21 120 EBF 25.85 0.77 ‐3.87 0.0% 100.0% ‐87% DG 1999‐2002 PT‐Mi2* 38.48 ‐8.02 190 GRA 14.37 1.63 ‐0.93 0.3% 99.7% ‐87% DG 2005‐2007 CN‐QYZ* 26.74 115.07 100 MF 18.59 1.30 ‐3.07 0.1% 99.9% ‐89% DG 2003‐2004 NL‐Ca1* 51.97 4.93 0.7 GRA 10.93 0.97 ‐4.40 36.9% 63.1% ‐90% DG 2003‐2004, 2006‐2007 ZA‐KRU* ‐25.02 31.50 300 SAV 21.78 2.72 0.25 0.0% 100.0% ‐90% DG 2001‐2005 US‐SO2 33.37 ‐116.62 1394 CSH 14.36 1.97 ‐0.54 0.1% 99.9% ‐91% DG 2004‐2005 AU‐How* ‐12.49 131.15 38 WSA 26.21 0.93 ‐3.60 0.0% 100.0% ‐92% DG 2001‐2005 US‐SP1* 29.74 ‐82.22 50 ENF 20.25 1.34 ‐1.99 0.0% 100.0% ‐93% DG 2001, 2003, 2005‐2006 CA‐Obs 53.99 ‐105.12 628 ENF 1.65 1.85 ‐0.55 59.6% 40.4% ‐97% DG 2000‐2006 FI‐Hyy 61.85 24.29 181 ENF 4.25 1.41 ‐2.09 39.7% 60.3% ‐97% DG 1997‐1999, 2001‐2004, 2006 The vegetation is coded according to the IGBP classification: CSH, closed shrublands; DBF, deciduous broad-leaf forests; EBF, evergreen broad-leaf forests; ENF, evergreen needle-leaf forests; GRA, grassland; MF, mixed forests; OSH, open shrublands; SAV, savannas; WET, permanent wetlands; WSA, woody savannas. PP(TG) indicates the posterior probability of each site belonging to the temperature group. PP(DG) indicates the posterior probability of each site belonging to the dryness group. RI refers to the residual index defined by the equation (3). BG stands for B group, TG for temperature group, and DG for dryness group. * indicates that NEE data was provided by the site P.I.. Supplementary figure S1. Geographical distribution of the sites in the three groups: temperature group, dryness group, and the B group. Supplementary figure S2. The latitudinal distribution of: (a) the T-group; (b) the D-group; and (c) the B-group. Supplementary figure S2. (Contnued.) Supplementary figure S2. (Contnued.) Illustration of Clustering Method 4 y 2 0 -2 -4 -4 -2 0 x 2 4 Supplementary figure. S3. Scatter plot of (x,y), ‘o’ stands for the samples from a bivariate normal distribution with mean (1.5,0) and covariance matrix being a diagonal matrix with diagonal elements 1 and 1, while ‘x’ stands for the samples from a bivariate normal distribution with mean (-1.5,0) and covariance matrix being a diagonal matrix with diagonal elements 1 and 1. Illustration of Mixture Regression 4 y 2 0 -2 -4 -4 -2 0 x 2 4 Supplementary figure S4. Scatter plot of (x,y). ‘o’ stands for the samples from y=x + e, while ‘x’ stands for the samples from y=x+e.. The dashed line is the line of y=x, and the dotted line is the line of y=-x.

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